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ðŸ“ ..
ðŸ“„ __init__.py(2.7 KB)
ðŸ“„ __init__.pyi(5.46 KB)
ðŸ“ __pycache__
ðŸ“„ _datasource.py(22.1 KB)
ðŸ“„ _iotools.py(30.14 KB)
ðŸ“„ _version.py(4.74 KB)
ðŸ“„ _version.pyi(633 B)
ðŸ“„ arraypad.py(31.06 KB)
ðŸ“„ arraypad.pyi(1.69 KB)
ðŸ“„ arraysetops.py(32.87 KB)
ðŸ“„ arraysetops.pyi(8.14 KB)
ðŸ“„ arrayterator.py(6.9 KB)
ðŸ“„ arrayterator.pyi(1.5 KB)
ðŸ“„ format.py(33.95 KB)
ðŸ“„ format.pyi(748 B)
ðŸ“„ function_base.py(184.67 KB)
ðŸ“„ function_base.pyi(16.2 KB)
ðŸ“„ histograms.py(36.81 KB)
ðŸ“„ histograms.pyi(995 B)
ðŸ“„ index_tricks.py(30.61 KB)
ðŸ“„ index_tricks.pyi(4.15 KB)
ðŸ“„ mixins.py(6.91 KB)
ðŸ“„ mixins.pyi(3.04 KB)
ðŸ“„ nanfunctions.py(64.23 KB)
ðŸ“„ nanfunctions.pyi(606 B)
ðŸ“„ npyio.py(95.04 KB)
ðŸ“„ npyio.pyi(9.5 KB)
ðŸ“„ polynomial.py(43.1 KB)
ðŸ“„ polynomial.pyi(6.79 KB)
ðŸ“„ recfunctions.py(58.03 KB)
ðŸ“„ scimath.py(14.68 KB)
ðŸ“„ scimath.pyi(2.82 KB)
ðŸ“„ setup.py(405 B)
ðŸ“„ shape_base.py(38.03 KB)
ðŸ“„ shape_base.pyi(5.06 KB)
ðŸ“„ stride_tricks.py(17.49 KB)
ðŸ“„ stride_tricks.pyi(1.71 KB)
ðŸ“ tests
ðŸ“„ twodim_base.py(32.17 KB)
ðŸ“„ twodim_base.pyi(5.24 KB)
ðŸ“„ type_check.py(19.49 KB)
ðŸ“„ type_check.pyi(5.44 KB)
ðŸ“„ ufunclike.py(6.18 KB)
ðŸ“„ ufunclike.pyi(1.26 KB)
ðŸ“„ user_array.py(7.54 KB)
ðŸ“„ utils.py(36.92 KB)
ðŸ“„ utils.pyi(2.3 KB)

Editing: arrayterator.pyi

from collections.abc import Generator
from typing import (
    Any,
    TypeVar,
    Union,
    overload,
)

from numpy import ndarray, dtype, generic
from numpy._typing import DTypeLike

# TODO: Set a shape bound once we've got proper shape support
_Shape = TypeVar("_Shape", bound=Any)
_DType = TypeVar("_DType", bound=dtype[Any])
_ScalarType = TypeVar("_ScalarType", bound=generic)

_Index = Union[
    Union[ellipsis, int, slice],
    tuple[Union[ellipsis, int, slice], ...],
]

__all__: list[str]

# NOTE: In reality `Arrayterator` does not actually inherit from `ndarray`,
# but its ``__getattr__` method does wrap around the former and thus has
# access to all its methods

class Arrayterator(ndarray[_Shape, _DType]):
    var: ndarray[_Shape, _DType]  # type: ignore[assignment]
    buf_size: None | int
    start: list[int]
    stop: list[int]
    step: list[int]

@property  # type: ignore[misc]
    def shape(self) -> tuple[int, ...]: ...
    @property
    def flat(  # type: ignore[override]
        self: ndarray[Any, dtype[_ScalarType]]
    ) -> Generator[_ScalarType, None, None]: ...
    def __init__(
        self, var: ndarray[_Shape, _DType], buf_size: None | int = ...
    ) -> None: ...
    @overload
    def __array__(self, dtype: None = ...) -> ndarray[Any, _DType]: ...
    @overload
    def __array__(self, dtype: DTypeLike) -> ndarray[Any, dtype[Any]]: ...
    def __getitem__(self, index: _Index) -> Arrayterator[Any, _DType]: ...
    def __iter__(self) -> Generator[ndarray[Any, _DType], None, None]: ...

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